Practical application of parallel coordinates for climate model analysis

Chad A. Steed; Galen Shipman; Peter Thornton; Daniel Ricciuto; David Erickson; Marcia Branstetter

doi:10.1016/j.procs.2012.04.094

Practical application of parallel coordinates for climate model analysis

Chad A. Steed, Galen Shipman, Peter Thornton, Daniel Ricciuto, David Erickson, Marcia Branstetter

Oak Ridge National Laboratory

Research output: Contribution to journal › Conference article › peer-review

22 Scopus citations

Abstract

The determination of relationships between climate variables and the identification of the most significant associations between them in various geographic regions is an important aspect of climate model evaluation. The EDEN visual analytics toolkit has been developed to aid such analysis by facilitating the assessment of multiple variables with respect to the amount of variability that can be attributed to specific other variables. EDEN harnesses the parallel coordinates visualization technique and is augmented with graphical indicators of key descriptive statistics. A case study is presented in which the focus is on the Harvard Forest site (42.5378N Lat, 72.1715W Lon) and the Community Land Model Version 4 (CLM4) is evaluated. It is shown that model variables such as land water runoff are more sensitive to a particular set of environmental variables than a suite of other inputs in the 88 variable analysis conducted. The approach presented here allows climate scientists to focus on the most important variables in the model evaluations.

Original language	English
Pages (from-to)	877-886
Number of pages	10
Journal	Procedia Computer Science
Volume	9
DOIs	https://doi.org/10.1016/j.procs.2012.04.094
State	Published - 2012
Event	12th Annual International Conference on Computational Science, ICCS 2012 - Omaha, NB, United States Duration: Jun 4 2012 → Jun 6 2012

Funding

This research is sponsored by the Climate Science for a Sustainable Energy Future (CSSEF) project that is funded by the U.S. Department of Energy, Office of Science. The authors wish to thank Zhangshuan Hou (Pacific Northwest National Laboratory) for the generation of the Harvard Forest site CLM4 ensemble data set. This paper was prepared by the Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT–Battelle, LLC, for the U.S. Department of Energy, under contract DE-AC05-00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Funders	Funder number
Climate Science for a Sustainable Energy Future
U.S. Department of Energy
Battelle	DE-AC05-00OR22725
Office of Science
Oak Ridge National Laboratory

Keywords

68U05
Climate model
Information visualization
Sensitivity analysis
Visual analytics 2000 MSC: 62P12

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10.1016/j.procs.2012.04.094

Cite this

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abstract = "The determination of relationships between climate variables and the identification of the most significant associations between them in various geographic regions is an important aspect of climate model evaluation. The EDEN visual analytics toolkit has been developed to aid such analysis by facilitating the assessment of multiple variables with respect to the amount of variability that can be attributed to specific other variables. EDEN harnesses the parallel coordinates visualization technique and is augmented with graphical indicators of key descriptive statistics. A case study is presented in which the focus is on the Harvard Forest site (42.5378N Lat, 72.1715W Lon) and the Community Land Model Version 4 (CLM4) is evaluated. It is shown that model variables such as land water runoff are more sensitive to a particular set of environmental variables than a suite of other inputs in the 88 variable analysis conducted. The approach presented here allows climate scientists to focus on the most important variables in the model evaluations.",

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author = "Steed, {Chad A.} and Galen Shipman and Peter Thornton and Daniel Ricciuto and David Erickson and Marcia Branstetter",

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AU - Shipman, Galen

AU - Thornton, Peter

AU - Ricciuto, Daniel

AU - Erickson, David

AU - Branstetter, Marcia

PY - 2012

Y1 - 2012

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KW - Information visualization

KW - Sensitivity analysis

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ER -

Practical application of parallel coordinates for climate model analysis

Abstract

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Keywords

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